Análise comparativa entre os indicadores econômico-financeiros aplicados às indústrias manufatureiras listadas na BOVESPA

Abstract

The financial statements provide useful information about the equity of the business units and their variations and performance, contributing to their various users in the decision- making process whether in economic, financial or operational dimension. In this sense, it can be noticed that the comparative analysis is one of the most common techniques for assessing performance in specific sectors and, thus, has always been a comparative technique. However, it adds further that more important than calculating a large number of indexes is to identify financial indicators that have greater adherence to the characteristics of business units and specific sectors. In this context, the use of statistical techniques becomes important in the investigation of indices or groups to assess specific sectors, thus contributing to improving the quality of information in support of decision making processes relating to business units when peculiarities of each one of them are considered. Therefore, the aim of this research was to identify which are the most relevant economic and financial indicators for monitoring the financial performance of each of the industries in the manufacturing sector and hence their relevance when applied to specific sectors. For this, we used factor analysis techniques from the indicators extracted from the financial statements, which are our sources of measuring the financial performance of companies. It selected 24 indices, collected through Economática® database, of public companies traded on the Securities, Commodities and Futures Exchange (BM&FBOVESPA) of manufacturing industry sectors, segregated in steel and metals, food and drinks, chemicals, textiles and motor vehicles aiming to identify the existence of common and specific indicators across sectors and subsectors. The results of the factor analysis showed that the relevant indexes to the manufacturing sector are grouped into seven factors with explanatory power of 77% of data variation. The research also showed no significant differences between the characteristics of the measurement indexes of manufacturing industries and their specific segments.